How to run OpenClaw on NVIDIA RTX and get the most out of it

La Artificial intelligence has crept into our daily lives At incredible speed: we use it for work, study, content creation, and automating all kinds of tasks. Until recently, it was common to rely on cloud services like ChatGPT or Gemini, but more and more people want to bring that power directly to their own computers, without depending on external servers or sharing sensitive data.

In that context it appears OpenClaw, a local-first AI agent which is generating a lot of buzz. It's not just "another chatbot": it's a system designed to be always active on your PC, reading the context of your files, email, and applications, and performing actions for you. And the best part is that, thanks to the NVIDIA RTX GPUs Thanks to the official guide published by NVIDIA, it's now possible to run it completely locally, taking advantage of your graphics card's Tensor Cores to achieve serious performance without leaving home.

What exactly is OpenClaw and why does it matter so much?

OpenClaw defines itself as a full AI agentDesigned to make decisions and perform tasks autonomously with a clear objective: to do for you everything you would normally do yourself in front of a computer. It doesn't just answer questions like a classic chatbot; it can connect with your applications, read documents, cross-reference information, and execute actions without you having to constantly monitor it.

The key lies in their approach local-firstOpenClaw “lives” on your own PC, runs in the background, and maintains a persistent context of your conversations, emails, files, and tools. In this way, it can remember what you were talking about yesterday, understand ongoing projects and relate information from both the internet and your local storage.

This project, initially created by Peter SteinbergerIt has gone through several names (Clawbot, Molbot) before becoming OpenClaw. Although it has ended up being acquired by OpenAI, its essence remains the same: Open source, free to use, and a strong focus on communitywith thousands of users building skills and expanding their capabilities.

One of the most striking things is the growth of its ecosystem: ClawHubThe community's central skills repository now exceeds 5.700 extensions, and the project has reached a record number of... 150.000 stars on GitHub in just a few weeks, something within reach of very few software projects.

To reinforce the security of this entire ecosystem, OpenClaw has a strategic alliance with VirusTotal, the well-known Malaga-based company owned by Google. Thanks to this collaboration, the community's skills are automatically analyzed to detect potentially malicious code before they can cause problems on users' equipment.

Why AI agents are the next step after ChatGPT

When talking about AI agentsThis refers to systems capable of acting autonomously, making decisions, and executing tasks without the user having to manually perform each step. In other words, They go a step further than a simple conversational model that only answers what is asked of it.

Unlike ChatGPT or Gemini, which typically run in the cloud and act as classic conversational assistants, an AI agent like OpenClaw It integrates seamlessly with your system: access your local files, monitor emails, check your calendar and can trigger actions in different applications.

This difference means that Privacy and data control take center stageSince it runs locally, the data doesn't have to leave your PC, which is especially interesting for professionals who handle sensitive information, regulated companies, or anyone who doesn't want to send half their hard drive to external servers.

Furthermore, the fact that OpenClaw is designed as always active agent It allows for continuous workflows: it can check your inbox, update projects, or generate summaries without you having to ask it every time, acting almost like a lifelong digital personal assistant, but on steroids.

What can OpenClaw do on your computer?

The list of real-world uses for OpenClaw is long, but some features clearly stand out. In day-to-day use, one of its strengths is... email automationThe agent can read previous threads, understand the context of a conversation, and generate responses or drafts that respect your tone and usual criteria.

It is also very powerful when it comes to manage agendas and calendarsOpenClaw can review invitations, suggest meeting slots, remind you of important appointments, and reschedule events based on your priorities or sudden changes in your schedule.

In the professional sphere, her ability to project managementThe agent can monitor documents, meeting minutes, pending tasks, and key dates, and from there remind you of milestones, propose next steps, or even generate status summaries to send to the team.

Another very interesting use is the combined researchOpenClaw can perform web searches and simultaneously cross-reference that data with reports, PDFs, spreadsheets, and notes stored on your PC. In practice, this is equivalent to having a system of RAG (Recovery Augmented Generation) fully localwhere the answers are fully tailored to your own private information repository.

Furthermore, the system is extensible through skills created by the communityThese range from integrations with specific tools to highly complex workflows. However, it is strongly recommended to use verified and well-reviewed skills for this purpose. avoid security problems or data leaks.

Security, privacy and usage recommendations

In order for OpenClaw to function effectively, it needs broad access to your dataEmails, local files, connected applications, etc. This opens the door to spectacular productivity, but also poses a clear risk if configured carelessly or if malicious skills are installed.

For that reason, many experts recommend Do not run OpenClaw directly on your main computer. If you store extremely sensitive information on it, it might be a good idea to opt for a secondary machine, a dedicated PC, or even a sufficiently powerful virtual machine, where you can grant broad permissions without fear of exposing your most sensitive data.

Integration with VirusTotal This plays a vital role: the more than 5.700 skills available on ClawHub undergo automated analysis to detect malicious software or suspicious behaviorEven so, the ultimate responsibility lies with the user, who should limit themselves to installing skills from reliable and reputable authors.

In terms of cost, it's simple: OpenClaw is free and open sourceThere are no paid licenses for the agent itself, although you will obviously need a computer with a capable GPU and, if desired, high-end hardware to be able to handle larger language models.

If used wisely and configured in a well-isolated environment, OpenClaw offers a very powerful combination of privacy, local control, and advanced automation, something difficult to find in purely cloud-based services.

NVIDIA RTX and OpenClaw: the guide to running it locally

NVIDIA has published a very detailed official guide explaining how to run OpenClaw completely locally on computers equipped with GeForce RTX GPUs, NVIDIA RTX Professional GPUs, and DGX Spark systemsThis documentation describes the entire process, from setting up the environment in Windows to choosing models and inference tools.

The proposal is based on taking advantage of the Windows Subsystem for Linux (WSL)This allows you to run a Linux environment within Windows with accelerated GPU access. Thanks to NVIDIA's drivers and CUDA stack, AI applications can directly utilize the Tensor Cores of the RTX to massively accelerate the calculation.

In practice, this means that OpenClaw can rely on model managers such as LM Studio u Don't to load and serve LLMs on your machine, while benefiting from specific optimizations such as Llama.cpp or CUDA integrationsThe result is much faster inference, with reduced latencies and efficient use of GPU memory.

According to NVIDIA's own guide, this approach allows scaling from relatively modest desktop PCs with a mid-range RTX to DGX Spark stations with massive memory, capable of smoothly running giant models with more than 100.000 billion parameters.

Furthermore, once configured, the agent can be controlled from Messaging apps like Discord, WhatsApp, or Telegramacting as an assistant that responds and performs tasks through conversations, which is very convenient if you spend your day between chats and channels.

Recommended models based on your GPU's VRAM

One of the key points when running OpenClaw locally is the graphics card video memory (VRAM)Depending on how much VRAM you have available, you can use smaller or much larger and more capable models. NVIDIA offers specific recommendations to ensure a smooth and stable experience.

For the range of 8 to 12 GB of VRAMTypical of many mid-range GeForce RTX GPUs, it's suggested to use models with around 4.000 billion parameters. An example is qwen3-4B-Thinking-2507, which combines a reasonable size with good reasoning capabilities and fast response times.

If your GPU has 16 GB of VRAMThe range expands to include larger models such as gpt-oss-20bThis type of model provides a deeper understanding of the context, better handling of long texts and more nuanced responses, ideal for those who use OpenClaw professionally with many documents and complex emails.

When we talk about cards or systems with 24 to 48 GB of VRAMHeavyweights like Nemotron-3-Nano-30B-A3BHere we are already in a league where the model can handle much more sophisticated reasoning tasks, huge contexts and multiple simultaneous workflows without getting bogged down.

At the higher end of the spectrum, the systems DGX Spark with 96 to 128 GB of memory They allow loading extremely large models, such as gpt-oss-120bThese models with more than 100B parameters are designed for demanding business environments, research laboratories or teams that need state-of-the-art language capabilities for multiple projects and users simultaneously.

In all cases, the RTX Tensor Cores They play a fundamental role: their architecture is optimized for mixed-precision operations used in neural networks, which translates into much faster and more efficient inferences than if everything were executed on the CPU.

WSL, LM Studio, Ollama and the technical stack behind it

The technical basis that NVIDIA proposes to take advantage of OpenClaw on Windows is based on WSL (Windows Subsystem for Linux)This component allows you to boot a lightweight Linux distribution within Windows, with direct access to the GPU through NVIDIA drivers and the CUDA stack.

Tools such as LM Studio or Ollamawhich act as language model servers. In practice, these utilities handle Download, quantize, and run LLMs, offering a local API or interface that OpenClaw can use to “think” and respond.

Many of these solutions internally utilize projects such as Call.cppThese models are heavily optimized to run efficiently on GPUs, with support for different formats, quantizations, and context sizes. Thanks to these optimizations, it is possible to achieve millisecond latencies even on well-configured home computers.

From the user's point of view, the flow is usually quite straightforward: you configure WSL, install the NVIDIA stack (drivers, CUDA), set up LM Studio or Ollama, download the recommended model for your VRAM, and finally, connect OpenClaw to that local server to use it as an AI engine.

From there, OpenClaw can run as a service that remains active and communicates with you through its own interface or integrations with messaging platforms and desktop applicationsdepending on how you have it configured.

Practical advantages of running OpenClaw locally with RTX

Beyond theory, the advantages of running OpenClaw directly on a NVIDIA RTX GPUs They are noticeable in everyday life. The first is the reduced latencySince it doesn't depend on the cloud, requests don't have to travel over the internet, and inference is done on your computer with the maximum possible acceleration, also allowing you to apply techniques for improve system performance.

The second major advantage is the privacyAll sensitive information—emails, internal documents, customer data, confidential reports—can remain on your machine, as the agent does not need to send anything to external servers to work, which is key for those with strict legal or regulatory compliance requirements.

There is also the factor of control and customizationBecause it's an open system running on your own hardware, you can adjust which models you use, how they're quantized, what context they handle, and how they integrate with your tools. You're not dependent on the limitations of an external API or unilateral changes from third parties.

Finally, if you already have a decent RTX card, the marginal cost of setting up OpenClaw locally is very low, especially compared to monthly subscriptions to cloud services or intensive consumption of paid APIs for large LLMs.

All of this makes the combination of OpenClaw + NVIDIA RTX It is a very attractive option for advanced users, developers, freelancers, and companies that want to invest in powerful AI agents while maintaining maximum control over their data.

Given all of the above, OpenClaw stands out as one of the most promising local AI agents of the moment: it combines a local-first approach focused on privacy, a very active community that contributes thousands of skills, integration with key tools such as LM Studio or Ollama, and explicit technical support from NVIDIA to run it completely locally on RTX GPUs and DGX Spark systemsWith the official guide to choosing the ideal model according to your VRAM, the alliance with VirusTotal to reinforce security, and the possibility of using it as a persistent assistant that connects to your email, calendar, projects, and chats, it becomes a very powerful solution for anyone who wants to take generative AI and agentive workflows to the next level without depending on the cloud.